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■ j|u Agriculture 



Canada 

Research Direction generale 
Branch de la recherche 



Contribution 1983-12E 




Modeling methodology for 
assessing crop production 
potentials in Canada 



i+i 



Agriculture 
Canada 



MAR - 4 1991 



Library / Bibliotheque, Ottawa K1A 0C5 




&A3& 



630.72 
C75S 
C tl-12. 

C-2> 




Canada 



The map on the cover has dots representing 
Agriculture Canada research establishments. 



Modeling methodology for 
assessing crop production 
potentials in Canada 



R.B. STEWART 
Agrometeorology Section 
Land Resource Research Centre 



Research Branch 

Agriculture Canada 

1981 

Reprinted 1983, 1986 



Copies of this publication are available from: 
Land Resource Research Centre 
Research Branch, Agriculture Canada 
Ottawa, Ontario 
K1A0C6 



Produced by Research Program Service 



© Minister ofSupply and Services Canada 1981 
Reprinted 1983, 1986 



CONTENTS 

Page 

Abstract iii 

Introduction 1 

Climatic Data 1 

Station Data 2 

Grid Square Data 2 

Soil Unit Data 2 

Definition and Calculation of the Growing Period 3 

Procedure for Estimating Crop Potential Net Biomass and Dry 4 
Matter Yield Production 

Calculation of Respiration Losses (C-p) 6 

Calculation of Gross Biomass Production (b_.,) 6 

GM 
Correction for Temperature 8 

Correction for Crop Development 10 

Calculation of Potential Dry Matter Yield 10 

Procedure for Determining Anticipated Net Biomass and Dry 12 
Matter Yields 

Calculation of Moisture Stress Losses 14 

Yield Response Factor (K ) 14 

Evaluation of ET A /PE y 14 

Evaluation of LAI 19 

Evaluation of the Workability Parameter 20 

A Sample Calculation 24 

Acknowledgements 27 

References 28 



Abstract 

The methodology by which the rainfed crop production potentials 
for various crops can be determined from generalized photo synthetic 
responses to average climatic factors of radiation, temperature and 
precipitation in Canada is presented. This bulletin provides details 
and assumptions on the crop growth model used to estimate the potential 
net biomass and dry matter yields. It also provides details on the 
assumptions made in development of the yield reducing agroclimatic 
constraint indices involving moisture stress and workability. These 
indices are applied against the potential values to estimate the 
agroclimatically attainable or expected net biomass and dry matter 
yields. The described methodology is not intended for predicting 
real time crop production values. Instead, it is designed as a land 
evaluation tool for assessing the long-term land resource capability 
for crop production on a continental basis. 



Resume 

Dans le present bulletin, on expose les methodes qui permettent 
de determiner les possibilites de production de diverses cultures en 
regime pluvial en fonction de leurs reactions photosynthetiques generales 
aux facteurs climatiques, notamment au rayonnement , a la temperature 
et aux precipitations. On y donne, en outre, des precisions sur un 
modele de croissance des cultures et sur les hypotheses relatives a 
ce modele qui sert a evaluer les rendements potentiels nets en biomasse 
et en matiere seche. Enfin, y figurent aussi des precisions sur les 
hypotheses concernant 1 ' etablissement des indices relatifs aux facteurs 
agroclimatiques qui reduisent le rendement , y compris le stress hydrique 
et les possibilites de travail. Ces indices sont appliques aux valeurs 
potentielles afin d' evaluer les rendements nets en biomasse et en matiere 
seche qu'il est possible d'atteindre ou qui sont prevus en tenant compte 
des facteurs agroclimatiques. Les methodes susmentionnees n ' ont pas 
ete mises au point pour la prevision ponctuelle des rendements des 
cultures; elles ont plutot ete congues comme outil d' evaluation des 
terres devant servir a estimer, a long terme et a l'echelle du continent, 
le potentiel des terres en production vegetale. 



- i i i - 



- 1 - 

Introduction 

F.A.O. projections estimate that to support the predicted world 
population in the year 2000 would require an increase in agricultural 
production of 60 percent. It is uncertain whether there are sufficient 
global land resources to accomplish this increase. At present, there 
is insufficient precise data upon which to base a reliable answer. 

F.A.O. began a study, in 197 6 involving global potential land use 
by agroecological zones. The aim of the project was to obtain a first 
approximation of the production potential of the world's land resources, 
and to provide the physical data base necessary for planning future 
agricultural development. 

Initially, the study dealt with rainfed production potential for 
eleven crops in developing countries. At the same time F.A.O. requested 
the cooperation of a number of developed countries, including Canada, 
in utilizing their methodology to evaluate production potential of 
various crops. This would serve as a test of the overall concept and 
would serve to expand the global data base. A project involving the 
assessment of the production potential of five crops, wheat, maize, soybean, 
potato and phaseolus bean, was begun in Canada in 1978 in response to this 
request. 

In brief, the F.A.O. (1978) procedures involved in assessing the 
rainfed crop production potential include the following: 

1) Inventorying the existing land resources for each region including 
the climatic resources and the soil resources; 

2) Matching the various climatic and soil requirements for each crop 
with existing land resources and calculating constraint free 
potential yields for each crop; 

3) Evaluating the anticipated yield potential by determining the yield 
reducing factors of a) moisture stress; b) losses due to diseases, 
pests and weeds; c) loss due to climatic variability affecting 
yield components and quality; and d) workability. 

A) Assessing the soil suitability of individual areas for the production 

of each crop. 
5) Comparing the agroclimatic suitability with the soil suitability 

assessment in evaluating the overall land suitability for the 

production of each crop. 

This paper documents the methodology used in evaluating the 
constraint free potential yield and the yield reducing constraints 
required in estimating the agronomically attainable or anticipated yields 
for wheat, maize, soybean, potato and phaseolus bean crops in Canada. 
Results of the inventorying procedures (point 1) and the modelling 
application (points A and 5) for Canada, however, are not presented 
here, but are presented elsewhere (Dumanski and Stewart, 1981). 

Climatic Data 

The procedures for estimating the potential and anticipated net 
biomass and dry matter yields described in the following sections are 
designed to evaluate the long-term crop production capability on a 
continental basis using basic climatic information. Basic data in this 



- 2 - 

instance refers to long-term monthly averages of climatic elements such 
as temperature, precipitation, incoming solar radiation, windspeed and 
vapour pressure. These data are readily obtained from observation 
networks or can be derived using simple empirical expressions. 

The basic climatic data source used in the calculation of potential 
and anticipated yields was the 1941-70 Canada Normals (Atmospheric 
Environment Service - Environment Canada) . Monthly normals provided by 
the AES were obtained in the form of actual station data and a 1290 
equal area grid system. 

Maximum and minimum air temperature and precipitation data were 
available from 1068 stations located throughout the country. Values 
for vapour pressure, windspeed and incoming global solar radiation 
were assigned to each station by superimposing the station locations 
onto a 1290 equal area grid square network covering the land mass of 
Canada. 

Grid square climatic information was obtained from the Atmospheric 
Environment Service - Environment Canada in the form of a 1290 equal 
area grid square network. For each grid square, climatic data including 
precipitation, mean, maximum and minimum air temperature (reduced to 
sea level), vapour pressures, windspeed and incoming global solar radia- 
tion, represented an area 100 km by 100 km. These data were derived 
by computer interpolation of climatic normals from over 1800 full and 
part-time climatological stations located throughout Canada (G. den 
Hartog, personal communication) . 

In this study soil information is integrated with the climatic 
and crop phenological information as a part of the overall assessment 
procedure. Since neither the station nor grid square locations could 
be used to adequately express the geographical distribution of soils 
the soil as a unit is used instead. More specifically, the Soils Map 
of Canada (Clayton et al., 1977) is used in this study as the basis 
for illustrating all computations geographically. Individual soil 
units defined by Clayton et al. are essentially those presented in the 
1:5 million Soils Map of the World (FAO, 1974) except that the Soils 
Map of Canada is a refined version of the Canadian portion giving more 
detailed subdivisions of the larger soil map units presented on the Soils 
of the World map. 

The soils Map of Canada consists of 755 specific soil units. 
Physical characteristics of each as well as the geographical extent 
are discussed in detail by Clayton et al. (1977). Climatic data for each 
of the 755 soil map units contained in the Soils Map of Canada were 
largely derived from the above grid square data. This was accomplished 
by superimposing the grid square framework onto the 1:5 million scale 
Soils of Canada Map and estimating the area of each soil unit contained 
in each grid square. From this the basic climatic data for each soil 
unit was obtained from a simple weighting procedure in the form: 






- 3 - 
n 



D K =S 1 (A Ki • V i )/A KT>> (1 > 

x=l 



where: D is the weighted arithmetic mean for soil unit K; 

i is a subscript denoting a grid square containing soil unit K; 
(i-n) represents the number of grid squares containing soil 
unit K; 

V is the climatic variable for grid square i; 
A^. is the area of soil unit K contained in grid square i; 
k^ is the total area of soil unit K. 
Using eq. (1) the entire grid square climatic inventory was converted to 
values representing individual soil map units. 

In certain instances the grid square data were considered inadequate 
for expressing the climatic information of various soil units particularly 
the mountainous area of British Columbia. To correct this situation 
station data was used to compute the climatic information for each soil 
unit by averaging the data for all stations contained in each soil unit. 
This was quite reasonable since the main intermountain agricultural 
areas contained one or more stations. For soil units containing no 
stations, however, the grid square data were used. 

Definition and Calculation of the Growing Period 

In order to estimate the potential for crop production the time 
period available for crop growth or growing period first has to be 
determined. The FAO study (1978) defined the growing period as the 
length of time during the year when precipitation exceeds half the 
potential evapotranspiration plus the period required to evaporate or 
reduce the water stored in the soil profile by 100 mm. Temperature is 
considered by reducing the number of days from the above moisture growing 
period over which the mean air temperature is less than 6.5 C. 

This is a moisture based definition and applies in areas where 
moisture rather than heat is the dominant yield controlling factor. 
In Canada, however, temperature is the major limiting factor to crop 
growth - not water. As a result, the growing period is defined from 
a thermal point of view based on the "frost free period". Specifically 
the growing season is defined as the period in days during the year 
when the mean minimum air temperature is greater than or equal to 5°C. 
Use of the 5°C isotherm for the start and end of the growing period 
represents with a 50 percent probability the average date, calculated 
from 30-year climatic normals data, for the last spring and first fall 
frosts (0°C) (Sly and Coligado, 1974). Moisture is not considered in 
the growing season definition since the effects of moisture shortages 
on crop development and yields is evaluated directly in the quantifica- 
tion of the moisture stress agroclimatic constraint. 



- 4 - 



The Julian dates for the growing season start and end were derived 
from the monthly minimum air tempreature normals data as follows: first, 
the monthly values were converted to daily values using the Brooks 
(1943) sine-curve interpolation technique. The Start and End of the 
growing season was then derived by computer interpolation of the dates 
the minimum air temperature first exceeded in the spring, and fell below 
in the fall, 5 C. From these dates the growing season length was computed 
as START-END+1. 

Daily values of maximum temperature, vapour pressure, incoming 
global solar radiation, windspeed and precipitation were generated 
from the monthly normals using the Brooks (1943) interpolation technique. 
Mean growing season values were then computed for each variable by 
summing the daily values from the start of the growing season to the 
end and dividing by the growing season length. In all cases since 
normals data are used, representing the 30-year period from 1941-71, 
it is assumed that the data is normally distributed throughout the 
month on both a weekly and daily basis. 

Procedure for Estimating Crop Potential Net Biomass and Dry Matter 
Yield Production 

The potential or maximum constraint free yield attainable by a 
crop is primarily determined by its genetic characteristics and how well 
it is adapted to the existing environment. Constraint free yield is 
defined as the harvested dry matter yield of a high producing variety, 
that can be produced under conditions where water, nutrients and weeds, 
pests and diseases do not limit crop growth. Under these conditions 
the crop yield is limited only by the crop physiological responses to 
the amount of radiant energy received by the crop, the temperature over 
the course of the growing period and the length of the growing period. 

Procedures developed by F.A.O. (1978) are used to compute constraint 
free net biomass production. Assuming no moisture, nutrient, weeds, 
pests and disease limitations to crop growth, the constraint free or 
potential net biomass production is computed as: 

B N = °' 36 b GM /(1/N + - 25C T } ' (2) 

where: b is the crop seasonal rate of maximum gross biomass production, 
C is a temperature function defining the crop maintenance 
respiration loss developed by McCree (1974) , and 
N is the growing season length. 

In the derivation of eq. (2) it assumed that: a) the cumulative 
potential growth rate of any crop over the course of the growing 
season is S-shaped as shown in Figure la; b) the change in growth 
rate over the course of the growing season is in the form of a normal 
distribution with the maximum rate occurring at the mid point in the 
crops life cycle (Fig. lb); and c) from (a) and (b) the seasonal 
average rate of gross biomass production is 50% of the seasonal maximum 
gross biomass production. As seen in eq. (2) if values of bQ^ and C T 
can be estimated the crop potential net biomass production can be 
derived by inserting the appropriate growing season length. The 
following sections outline the procedures used to evalute Cf and bz-j^ 
in this study. 



- 5 - 



FIG 



A) 



CO 



o 

S- 

Q_ 
O 

S- 
(J 

> 






1= B. 



B= B r 



K 



Slope at the point of 
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-XD 



CO 



fa 

ST. 

+-» 

6 

S- 

cr> 

CL 

o 

s- 




Time (t) 



/.«/?/ 



Fig. 1A Typical cumulative crop qrowth curve showing the point of 
inflection during the period of maximum growth when the 
slope dB/dt is equivalent to the maximum rate of net biomass 
production (b nm ). 

Fig. IB The normal shape of the curve of crop growth rate plotted 

against time showing average crop growth rate (b na ) = 0.5b nm 



- 6 - 



Calculation of Respiration Losses (C ) 

Values of C were obtained using the expression developed by 
McCree (1974) : 

C = C (0.044 + 0.0019T + 0.0010T 2 ), (3) 

where: T is the mean air temperature, and 

C30 is the maintenance respiration coefficient at 30 C. 
At 30°C, McCree (1974) observed values for C 30 of 0.0283 and 0.0108 for 
a legume and non-legume crop, respectively. The former is used in the 
determination of Bjj for soybean and phaseolus bean, while the latter is 
used for corn, wheat and potato. 

Calculation of Gross Biomass Production (b„) 

GM 



The method developed by deWit (1965) is used to evaluate bg-^. 
Using this approach, a crops maximum rate of gross biomass production, 
described by a characteristic set of standard variables at an assumed 
leaf area index of 5.0, can be determined for any location as: 

b GM = F X b o + (1 " F) X V (4) 

where: F is the fraction of the day that the sky is overcast, 

b is the rate of biomass production on overcast days, and 
b is the rate of biomass production on perfectly clear days. 

The fraction of the daytime when the sky is overcast, (F) , is 
evaluated from the expression: 

F = (PAR - 0.5k+) / 0.8 PAR , (5) 

c c 

where: PAR is the photosynthetically active radiation on perfectly 
clear days, and 
k4- is the incoming global shortwave solar radiation. 

Estimates of PAR , b Q and b determined by deWit (1965) for a 
crop, with a maximum rate of CO2 exchange (P m ) of 20 kg ha - hr~ , were 
used in the calculation of b~ M in equation (4) . Data representing the 
mean monthly values over the latitudinal extent of Canada are given in 
Table 1. In his calculations, deWit assumed that the photosynthetically 
active radiation on totally overcast days is 20 percent of PAR C , and 

that PAR is 50 percent of k4- in both clear and overcast conditions. 

c v 

Values of b and b c , illustrated in Table 1, as mentioned above 
represent computed values for deWit's standard crop. Under actual 
field conditions, these photosynthetic rates in many instances can be 
exceeded or not reached at all depending on how well the actual crop 
growth compares to deWit's standard crop. Therefore, b„ M values obtained 
from eq. (4) must be corrected for the various factors causing divergence 
of the standard crop from actual crop performance. In the FAO methodology 
two factors involving temperature and crop development are used to correct 
deWit's standard crop estimates. These are outlined in the following 
sections. 



- 7 - 






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8 - 



Correction for Temperature 

The maximum rate of CO2 exchange (P max ) and, therefore, the gross 
biomass production is dependent on both the photosynthetic pathway of 
the species and the temperature at which the crop photosynthesizes. 
Since the deWit's standard crop assumes P m = 20 kg ha~lhr~^, crops 
maintaining maximum production rates differing from this have to be 
corrected either up or down depending on the comparison of the crops 
actual P m to deWit's value. Correction criterion used in this study 
are as follows: if P m >20kg ha hr~ , bg^ is increased by: 

Y/5/100 x f x b + Y/2/100 x (1-F) x b , (6a) 

o c 

van Ittersum (1972), F.A.O. (1978); while if P m <20 kg ha^hr" 1 , b 



is decreased by: 



GM 



Y/2/100 x F x b + Y/100 x (1-F) x b . (6b) 

o c 

In the above equations the parameter Y, representing the percentage 
by which the crop maximum photosynthetic rate (P m ) exceeds or falls 
below the standard crop value of 20 kg/ha/hr, is estimated as: 

Y = ((P - 20))/20) x 100, (7) 

ma 

and P is the actual crop maximum photosynthetic rate. 

ma 

Values of P ma in this study were derived from relationships 
developed by F.A.O. (1978). Curves illustrating the relationships 
of P with temperature for the two main crop groups, containing the 
5 crops considered in this study, are illustrated in Fig. 2 as taken 
from F.A.O. (1978), Fig. 3.1. Polynomial expressions were fit to each 
of the curves depicted in Fig. 2 facilitating computer calculation of 
the maximum photosynthetic rate for each group. The derived expressions 
were as follows: 

GROUP IP = -11.308 + 3.524 T - 0.097 T M _ T 2 , (8a) 
ma. MDT MDT 

and GROUP IV P = -433.67 + 48.67 T _ - 1.576 T ^ + 

ma, MDT MDT 

0.017 Tmdt 3 , (8b) 

where: T^-p is the mean daytime temperature calculated from the 
mean (T mean ) , maximum (T max ) , and minimum (T m ^ n ) temperature data in 
the form: 

T _ = T + (2(T -T )/3.1416). (9) 

MDT mean max mean 

P m values for wheat, soybean, potato and phaseolus bean crops were 
obtained using eq. (8a) while values for corn were computed using eq. (8b). 



- 9 - 



FIG. 2 



70 






o 

CM 

o 

CD 



E 



OO 



on 



o 



o 



x 



60 



50 



40- 



30- 



20- 



10 - 




\ 



\ 



IV 
\ 



\ 



\ 



10 



15 



20 



25 



30 



35 



40 



MEAN DAYTIME TEMPERATURE (°C) 



Fig. 2 Average relationship between maximum leaf photosynthesis 
rate and temperature for crop groups I and IV. (from 
Fig. 3.1, F.A.O., 1978) 



LRRI 



Correction for Crop Development 

For a given value of P ma the magnitude of bg^ depends on the Leaf 
Area Index (LAI), which is an expression of crop development, representing 
the fraction of the soil surface covered by the crop. Since deWit's 
standard crop assumes that LAI = 5.0 in the calculation of P , if the 
actual crop LAI is less than 5.0, then bg^ has to be corrected for 
this difference. F.A.O. (1978) has derived a relationship relating 
the maximum crop growth rate (MCGR) to LAI. A polynomial expression 
fit to the relationship illustrated in F.A.O. (1978), Fig. 7.3 in 
the form: 

? 
MCGR = 0.004 + 0.316 LAI - 0.032 LAI , (10) 

is used to correct the maximum crop growth rate in the event the 
actual crop LAI<5.0. When the LAI>5.0, MCGR is set equal to one. In 
this situation it is assumed that the effect of LAI on bg^ is negligible 
since the coincidence of complete ground cover and light interception 
is achieved by the crop canopy at this point in time. 

With the inclusion of the correction for crop development the 
"actual" crop potential net biomass production (\ TArT ) for a crop of 
N days can be determined as: 

B NACT ■ B N X MC0R - (11) 

where: B is obtained from eq. (2) and MCGR from eq. (10), respectively. 

Calculation of the Potential Dry Matter Yield 

The potential net dry matter yield is obtained from the net biomass 
production values by taking into account the appropriate harvest index (Hj) 

B = B^ T x H . (12) 

y N ACT T 

Hx the harvest index, is defined as the fraction of the crop net biomass 
production that is economically useful. Harvest index values for each 
of the crops listed in Table 2 were obtained from F.A.O. (1978). 
These figures represent the range of values to be expected over various 
ranges in the growing season length. From these data Hy values 
representing the exact growing season length were derived by linear 
extrapolation between the minimum and maximum values recorded for each 
growing season length range. 

It should be remembered that factors such as the genetic potential 
of the crop cultivar, moisture conditions and farming practices can 
cause the value of Hj to fluctuate considerably from yar to year. For 
this reason values listed in Table 2 are assumed representative of the 
long-term averages corresponsing to those that would occur with little 
or no agronomic constraints (F.A.O., 1978). 



- 11 - 



TABLL 2. Crop Characteristics Considered in the Potential Net Biomass and 
Yield Calculations 









7 5-89 


Growing Seasor 


i Length 
120-149 


(Days) 






Crop 


90-119 


__JAQrl7JL. 

1 10 





180 


Spring Wheat 


GSL* 


i in 


J 10 


no 


110 






LAI 


3.1-3.7 


3.7-5.0 


3.7-5.0 


3.7-5.0 


3. 


7-5.0 






HI 


0.11-0.28 


0.29-0. 40 


0.40 


0.40 


0.40 






DMMP 


.85 


.85 


.85 


.85 




.85 


Maize 




GSL' 1 ' 


130 


130 


130 


130 




130 






LAI 


2.5-3.0 


3.0-4.0 


3.7-4.0 


3.7-4.0 


3 


.7-4.0 






HI 


0.22-0.15 


0.15-0.35 


.35 


.35 




.35 






DMMP 


.85 


.83 


.83 


.83 




.83 


Soybean 




GSL* 


130 


130 


130 


130 




130 






LAI 


2.5-3.0 


3.0-4.0 


3.0-4.0 


3.0-4.0 


3 


.0-4.0 






HI 


0.2-0.29 


.30 


.30 


.30 




.30 






DMMP 


.85 


.85 


.85 


.85 




.85 


Potatoes 




GSL* 


140 


140 


140 


140 




140 






LAI 


2.5-3.0 


3.0-4.0 


3.0-5.0 


3.0-5.0 


3 


.0-5.0 






HI 


0.45-0.59 


.60 


.60 


.60 




.60 






DMMP 


.32 


.32 


.32 


.32 




.32 


Phaseolus 


Bean 


GSL* 


120 


120 


120 


120 




120 






LAI 


2.5-3.0 


3.0-4.0 


4.0 


4.0 




4.0 






HI 


0.19-0.29 


0.3 


0.3 


0.3 




0.3 






DMMP 


.85 


.85 


.85 


.85 




.85 



LAI & HI - From F.A.0. (1978) Tables 7.4 and 7.6 

••'GSL is the maximum growing season length required to mature the crop under 
average Canadian conditions. 

LAI is the average Leaf area index during the growing period 

HI is the average harvest index 

DMMP is the percent dry matter in the main product. 



- 12 - 



Procedure for Determining Anticipated Net Biomass and Dry Matter Yields 

Net biomass and yield as outlined above provide estimates of the 
potential which can be expressed under conditions that are free from 
yield reducing factors (constraints) within the growing period. In 
deriving actual anticipated yield values, however, yield losses due to 
various agroclimatic constraints must be considered. According to the 
FAO (1978) methodology yield losses in rainfed crop production are 
governed by agroclimatic constraints involving: moisture stress; pests, 
diseases and weeds; water stress, pests and diseases, and climatic effects 
on yield components, yield formation and quality of produce; and work- 
ability constraints. These constraints, as pointed out by F.A.O. (1978) 
are complex and dynamic and their interrelations are extremely difficult 
to assess quantitatively. For this reason values were arbitrarily 
selected to represent the constraints in the various Agro-Ecological 
Zones of Africa. Selection of the constraint values was based on a 
bulk figure reduction. For example, if the particular constraint was 
negligible a value of was assigned; if the constraint was moderate a 
25 percent value was assigned while a value of 50 percent was given if 
the constraint was considered severe. Values representing the various 
constraints were assigned to each region considering their effects on 
both high and low input farming practices. 

This study deviates from the F.A.O. methodology in that two of the 
constraints are assessed quantitatively. These include yield losses due 
to moisture stress and those due to workability. Losses in Canada due to 
the effects of diseases, pests and weeds are assumed negligible as a 
result of high input farming practices, which for the most part have 
kept yield losses to less than 15 percent (W. Saidak, personal communi- 
cation). Similarly yield losses due to climatic constraints on yield 
components, yield formation and quality of produce were ignored since 
it is believed that losses associated with this constraint are in part 
taken into account in the quantification of losses due to moisture stress 
and workability. Following this the actual or anticipated net biomass 
(B ) production for each crop was calculated as 

x MSF x WP, (13) 



B 


B 


ANT 


N.^ 




ACT 



and the actual of net dry matter yield as: 

B Y = B ANT X H r (14) 

Y ANT AJNi L 

MSF and WP are respectively the yield losses atrributed to moisture 
stress and workability. The following sections outline the techniques 
used in this study for estimating MSF and WP. 



- 13 - 



T ABLE 3 - Yield Resp onse Factor to Moisture for Canadian Crop Condition s 

Crop Yield Response Factor (K ) 

Wheat 1.15 

Corn 1.25 

Soybean 1.20* 

Potato 1.10 

Phaseolus Bean 1.15 

From - Table 2. Doorembos and Kassam (1979). 



* - Kassam (personal communication) . The values expressed in 
Table 2 of Doorembos and Kassam (1979) represent cultivars 
grown in tropical and subtropical conditions. Temperate 
cultivars typically grown in Canada are much more sensitive 
to moisture stress. 



- 14 - 



Calculation of Moisture Stress Losses 

An expression relating the relative yield decreases to the 
relative evapotranspiration deficit was used to quantify the effect 
of moisture stress on yield losses. The relationship is expressed 
in the form 

(1 - \) = (1 - ^A) K (15) 

Y p PE 
where: Y is the actual harvested yield, 

Y is the potential or maximum harvested yield, 
ET is the actual evapotranspiration, 
PE is the potential or maximum evapotranspiration, and 
K is an empirically derived yield response factor. 

Rearranging equation (15) the actual crop yield Y can be computed 

ET 
Y. = Y (1-K (1- A) = Y_ x MSF, (16) 

A P y pE P 

ET 
where: MSF = 1- K (1 - A) (16a) 

7 PE 

is the moisture stress factor (MSF). Therefore, if K^, ET A and PE are 

known or can be estimated, yield reductions due to moisture stress can 

be determined from equation (17). The following section discusses the 

procedures used to evaluate these parameters. 

Yield Response Factor (K ) 

: y_ 



as: 



Values of the yield response factor (K ) used in the solution of 
equation (17) for each crop are listed in Table 3 as taken from Doorembos 
and Kassam (1979). These values assume that the relationship between 
relative yield (Y^/Yp) and relative evapotranspiration (ET^/PE) is linear 
and is applicable for moisture deficits up to 50 percent, i.e. 
(1 - ET^/PE = 0.5). Where moisture deficits exceed this limit it is 
assumed that the linearity of these relationships remains constant. 

Evaluation of ET./PE 
A 



The relative moisture deficit was evaluated using a soil moisture 
budgeting procedure expressed in the form 

GSE 

.,£ (P. + AS - R.) 

i=GSS \ \_ 

ET/PE = GSE PE. (18) 

A I 

i=GSS 



- 15 - 

where: GSS is the growing season start (Julian date) , 
GSE is the growing season end (Julian date) , 
P is the daily precipitation, 
R is the daily runoff, 
PE is the potential evapotranspiration, and 
I'lAS is the change in available moisture storage between the 
start of the growing season and day i. 

The available soil moisture in storage during the growing season is 
monitored on a daily basis using the expression 

S = S -PE x (AE / PE„ ) - PEL x (AE / PEL ) + P - R, , (19) 
1 l— ± a. s o. r. p r. l l 
l i l r l 

where: S. is the available soil moisture at the end of the day, 

S._, is the available soil moisture at the beginning of the day, 

PE is the potential evaporation from bare soil surface, 

PE is the potential transpiration by the crop canopy, 

AE /PE 1 ^ is the ratio of actual to potential bare soil evaporation, and 

AE /PE is the ratio of actual to potential plant transpiration. 

In the solution of equation (18) and (19) the procedures developed 
by Ritchie (1972, 1974) describing the partitioning of evapotranspiration 
between base soil evaporation and plant transpiration for a developing 
row crop was used. 

Potential evapotranspiration above the crop canopy (PE) is 
computed first, using the combination equation of Penman (1963) in the 
form: 

PE = (S/a)Q* + 0.262 (1+0.0061 U) (e -e ) (S+l)" 1 (20) 

s a — 

a 

where: S is the slope of the saturation vapour pressure curve at the 

mean air temperature (T ) , 

a is the psychrometric constraint, 

U is the windspeed at the 2m height, 

e is the saturation vapour pressure at T , and 

s m 

e is the mean vapour pressure of the air. 

For the purpose of this study the soil heat flow is assumed 
negligible over the course of the growing season and is ignored in 
the calculation of PE. 

Net radiation above the crop canopy (Q*) is estimated using the 
empirical relationship (Ritchie, 1974). 



Q* = (0.77K++(0.00414(T ni -7.75) 1 - 8 ((1.35K+/K4- c )-O.35)-2.61))59. (21) 



- 16 - 



where Ki is the incoming global solar radiation measured above the 
crop canopy (mm of water equivalent) , 
K> is the incoming global radiation expected for perfectly clear 
skies (mm of water equivalent), computed as K+ c = 9.4 - 3.34 
sin (0.986(J-80)) where J is the Julian date and the argument 
of the same function is in degrees. 

Potential base soil evaporation below the crop canopy (PE ) was 
calculated as 

PE = 0.8 (-)Q* + 0.262 (1+0.0061 U ) (e -e ) (- + l)" 1 , (22) 
s a x s s o a a 

where: Q* is the net radiation below the crop canopy determined as 

S Q* = Q* AC exp (-0.4 LAI), and 

U is the wmdspeed corrected from the 2m height to the surface 

S where U = U* (0.4 LAI), 
s 

Ritchie (1974) used an empirical expression to distinguish potential 
bare soil evaporation from potential plant transpiration in the form 

i 
PE = PE x (-0.21 + 0.70 LAI 2 ). (23) 

P 

Equation (23) is valid for leaf area indexes (LAI) in the range 0.1 to 
2.7. When LAI is greater than 2.7 Ritchie found that PE was independent 
of LAI and equation (23) reduces the PE = PE. P 

Actual evaporation (AE ) and transpiration (AE p ) values from the 
soil surface and plant canopy were obtained using an extrapolation 
technique wherein the ratio of actual to potential evaporation is 
related to the available soil moisture in storage as shown in Fig. 3. 

In the budgeting procedure described by equation (18) and (19) , 
4 soil moisture holding capacities have been selected to represent 
various soil textural classes. For example, soils characterized as 
sandy loam were assigned a maximum available water capacity of 100 mm; 
very fine sandy loam and loam 150 mm; silt loam and clay loam 200 mm; 
and silty clay loam through heavy clay 280 mm. The moisture release 
curves illustrated in Fig. 3 were used in the moisture budget calculation 
procedure as follows: 

a) curve C assumes that none of the soil water is readily available, 
and curve G and H assumes 30 and 50 percent is readily available, 
respecticely . 

b) for all 4 moisture capacities "bare soil" evaporation is characterized 
by curve C. 

c) plant transpiration from soils with 100 and 150 mm capacities is 
characterized by curve H, while transpiration from 200 and 280 mm 
soils is described by curve G. 



- 17 - 



Fig. 3 



100 



AE 

PE 



(%) 




AVAILABLE SOIL MOISTURE (%) 



Relationships between AE:PE and available soil moisture 

(From Baier & Robertson, 1966) 

(Fig. 2, Baier eta!., 1979) lrri 



- 18 - 



Also the crop rooting depth was considered to be infinite in this 
study with the absolute moisture content being the sole limiting factor 
in dictating crop water availability. To air in distinguishing between 
plant and soil evaporation the soil continuum was broken into two zones: 
a "surface" layer or zone and a subsurface layer referred to as the 
"plant zone". The surface zone was assumed to contain 25 percent of the 
total soil moisture holding capacity and both bare soil and plant transpi- 
ration were allowed to draw moisture from this zone. The plant zone was 
assumed to contain the remaining soil moisture and only plant transpira- 
tion was allowed to extract water from this zone. 

To simulate plant development effects on the crops ability to draw 
moisture from the deeper plant zone it is assumed that plant transpiration 
occurs only from the "surface zone" until the LAI reaches a value of one 
(LAI=1) . At this point the crop canopy has developed to the extent that 
it approaches total cover of the soil surface and at the same time root 
development has expanded to the depth where it can tap the moisture 
reserves in the subsurface "plant zone". 

Ordering of various events has been assumed in the calculations. 
Plant transpiration takes place before bare soil evaporation; runoff 
occurs when water input to the surface through precipitation exceeds 
the total soil moisture holding capacity; and precipitation and runoff 
are assumed to occur at the end of the day. 

The water budgeting begins on the date when PE first exceeds 
precipitation (P) , herein, referred to as the Moisture Growing Season 
Start (MGSS) . Calculation of this date was accomplished in the following 
manner. First, monthly values of PE were derived utilizing eq. (2). 
In all cases the monthly normals as previously mentioned are assumed to 
be notmally distributed on both a weekly and daily basis. From these 
data daily values of PE and P were generated using the Brooks (1943) 
sine curve interpolation procedure. Plotting the daily values of P 
and PE the date that PE first equalled or exceeded P was obtained from 
the intersection of the two daily curves. 

A different method was used to determine the MGSS in the case where 

the soil remained frozen after the data PE exceeded P. From work on 

winter hardiness of alfalfa at various locations throughout Canada 

(Ouellet - personal communication) data on the last date of complete 

snow cover removal was obtained. Assuming that surface moisture loss 

through bare soil evaporation is negligible until the soil surface 

completely thaws, it follows that the surface remains frozen until the 

snow cover has been completely removed. Comparison of Ouellet' s data 

with the date the mean air temperature first exceeded C showed that 

complete snow cover removal occurred approximately 9 days later. It 

was assumed that the soil surface has thawed at this point and that 

evaporation occurred normally. Therefore, in the case when the date PE 

first exceeds P occurs before the date the mean air temperature (T ) 

exceeds C the moisture growing season start was set equal to the date 

T = 0°C plus 9 days (i.e., if PE > P and T < 0°C then MGSS = T +9). 
m F J » - m - mo 



- 19 - 

The total soil moisture in storage at the moisture growing season 

start (S Mrqq ) , was computed using the annual ratio of precipitation to 

PE, (P/PEJ. If P/PE>1, S was set at the assigned field capacity 

(S^J (i.e. 100, 150, 200, 280 mm). When P/PE<1.00, S_,„_ e was reduced 

to F rs„ x P/PE). MGSS 

FC 

The available soil moisture was not assumed to be reduced equally 

in both the surface and plant zones. Instead, since soil moisture 

recharge occurs in the surface zone first, eventually working down to 

the deeper soil layers, this manner of recharge was simulated by assuming 

that the total reduction in available soil moisture was reflected 

exclusively in the plant zone. The surface zone was assumed to be at 

field capacity. Therefore, at S,,„_„ the total available moisture in 

, MGSS 

storage was computed as: 



S MGSS = S FC X ' 25 + ' 75 X SF c " (SF c X Cl-AE/PE)) = S g + S p , (25) 

where: S is available soil moisture in the surface or soil zone, 
s 
and S is the available soil moisture in the plant zone. 
P 

Evaluation of LAI 



Crop development was simulated over the growing season by the Leaf 
Area Index (LAI) . Daily LAI values for each crop were derived from the 
expressions: 

3 111 
LAI = 2.77E-05 x GSL , (26a) 



for the first 42 days of the growing season, and thereafter as: 
LAI = 6.691-0.9106 GSL+.0398 GSL 2 -6.529E-04 GSL 3 



(26b) 



4 5 

+4.693E-06 GSL -1.257E-08 GLS , 



for the remaining portion of the crops life cycle. Ecuation (26a, 

26b) were developed by fitting polynomial expressions to the average 

curves depicting LAI vs time for spring wheat (Watson, 1971) and potato 
(Thorns, 1971). 



- 20 - 



The general relationship depicted by eq. (26) was assumed to be 
similar for all crops considered in this study since they represent a 
crop file cycle of approximately 130 days including 10 days from 
planting to emergency. For example, the life cycle of all crops 
considered varies from 108 days for wheat to a maximum of 140 days for 
potato; the remaining crops all reach maturity approximately 120 days 
after seeding. In this manner the shape of the curve depicted by eq. 
(26a) is virtually identical for all crops. 

When 0<_LAI<2.8, this parameter plays a significant role in 
splitting total evapo transpiration between bare soil evaporation and 
plant transpiration; when LAI>2.8, however, all of the moisture loss 
at the surface is accounted for by transpiration. Consequently, 
errors in estimating LAI by equation (26), and the subsequent 
effects of these on the ratio of growing season AE/PE, are minimal 
allowing one expression of LAI to represent all crops. 



Evaluation of Workability Parameter 

The agroclimatic constraint, workability, was derived from estimates 
of fall workday probabilities obtained from a model developed by Baier 
et al. (1979). The workday concept is related to workability in that 
it defines the risk associated with having only a minimum number of days 
to complete the harvest before the onset of inclement weather. The risk 
factor is related to the length of the growing season by the assumption 
that the greater the growing season in relation to the crop growing season 
requirements the more time is available for crop harvesting (conversely 
there is less risk of not completing the harvest) . This relationship 
is simulated using a probability description for the harvest period in 
the form 

N 
W = 1 - E WD./(N-i), (27) 

L i=GSE X 



where: W is the workability yield loss, 

L is a subscript denoting the probability level selected in 
the evaluation of W, 
WD is the workday probability computed by the Dyer and Baier 
(197 9) model, and 
i and N are subscripts denoting the Julian dates of the beginning 
and end of the crop harvest period and (N-i) is the length of 
the harvest period. 

Workability losses derived in equation (27) are inferred directly from 
the fall workday probability calculations through the assumption that these 
values reflect the probability that a given portion of the crop will be 
harvested in the time available. By this it is meant that if a farmer 
requires a certain number of workdays and the probability of getting this 
number of days is for example 70 percent, then on average only 70 percent 
of the crop is harvested over the long term. 



- 21 - 



TAULE A 



C RO_I'_W< )RKD.AY LEVEL CRITERION USED T N J '. V ALU ATI OX OF 

CROP LOSSES DUE TO WORKABILITY CONSTRAINTS 



Average Frost Workday Probabi I ilv Level (L) 
Fret; Growing Relat ion ship with Crowing 

Crop Per iod Requ i rement. Season L e ngth (CSL) 

Corn 130 If CSL :■ 140 days L - 50% 

it 120 < CSL < 140 L = 70% 



Soybean 



If CSL • 120 I, = 1007. 



Spring Wheat 110 If CSL _• 120 days L = 507 

Potatoes H~ 100 ■ CSL 120 L = 707 

Phas. bean If CSL -100 L = 1007 



- 22 - 



In the above relationship the required harvesting time "X out of 
N days" is defined as the workday probability level (L) . As mentioned 
previously, L is related directly to the length of the growing period. 
The relationship between L and GSL is inverse with the value of L 
increasing as the growing season length decreases. 

The growing season length effect on L was selected using the 
following criterion for each crop in this study: if the growing season 
length exceeded the crop average growing season requirements by 10 days 
or more, L was set at 5 out of 10 days (50%); if the GSL was within ±10 
days, L was set at 7 out of 10 days or 70%; and if GSL was 10 days or 
more short of the average crop requirement L was assigned a 10 out of 
10 day or 100% value. The specific criterion used in evaluating the 
workday probability level for each crop is shown in Table 4. 

The fall workday probability estimation procedure of Baier et al. 
(1979) involves a soil moisture budgeting procedure that relates the 
near surface soil moisture to farm machine tractability or slippage. 
Therefore, depending on the surface soil moisture content each day is 
classified as a workday or non-workday. Probability estimates are 
derived from these values by applying a simple count and sort procedure 
involving several years of data. 

For the purpose of this study frequency distributions of workdays 
expected in consecutive 10 day periods between May 1 and November 31 
were generated using daily historical data for 44 meteorological stations 
throughout Canada. Tables were generated for each station representing 
the probability of having 1 through 10 workdays cut of a possible 
10 in increments of 10 day periods. 

Combining these results with the criteria listed in Table 4 work- 
ability estimates were determined for each crop by averaging the proba- 
bilities for the 10 day periods enclosed in the harvesting period for the 
crop in question. The harvesting period was assumed to be 40 days in 
length for corn and soybens, and 20 days for spring wheat, potato and 
phaseolus bean. For all crops the harvesting period begins immediately 
following the date the crop reached maturity or the growing season end, 
whichever occurred first. An example of the calculation procedure for 
this parameter for two meteorological stations for wheat and corn is 
outlined in Table 5. 

The derived workability figures for each station were mapped for 
each crop and superimposed onto the 1:5 million scale soils map of 
Canada. Workability values for each soil unit were then estimated by 
interpolation. 



tabu: 5 



- 23 - 



CALCULATION PROCEDU RE FO R ESTIMATING THE WORKABILITY 
CROP LOSSES AT HARROW, ON TARIO AND BRANDON , 
~ MANITOBA FOR SPRING WHEAT AND MAIZ1-: 



: l r °hf i ^i-lity Level 



Harr ow, Ont ario 
50% 70% 100Z 



Brandon, Manitoba 
50% ' 70% 100% 



May 1 


- 


May 


11 


May 12 


- 


May 


21 


" 22 


- 


ti 


31 


June 1 


- 


June 


10 


" 11 


- 


ii 


20 


ii 2\ 


- 


M 


30 


July 1 


- 


July 


10 


" 11 


- 


ii 


20 


" 21 


- 


ii 


30 


" 31 


- 


Aug 


9 


Aug 10 


- 


Aug 


19 


" 20 


- 


H 


29 


Sept 9 


- 


Sept 


8 


- 


Sept 


18 


" 19 


- 


ii 


28 


" 29 


- 


Oct 


8 


Oct 9 


- 


Oct 


18 


" 19 


- 


ii 


28 


" 29 


- 


Nov 


7 


iov 8 


- 


Nov 


17 


" 18 


_ 


ti 


27 



93 
91 
98 
100 
100 
100 
100 
96 
98 
98 
98 
96 
98 
96 
96 
91 
84 
82 
51 
49 
42 



wheat 



corn 



76 
87 
96 
96 
98 
93 
96 
91 
93 
89 
93 
91 
93 
89 
84 
76 
73 
69 
38 
29 
18 



36 
47 
51 
51 
49 
47 
58 
51 
58 
49 
67 
49 
58 
58 
42 
56 
56 
36 
29 
16 
4 



95 
97 
96 
95 
99 
96 
95 
100 
99 
97 
96 
99 
91 
96 
96 
96 
97 
97 
96 
96 
96 



87 

93 

93 

85 

92 

89 

91 

92 

93 

93 

93 

93 

85 

88 

84 

85 

89 

92 

93 

93 

96 



60 

67 

59 

48 

57 

47 

49 

60 

69 

61 

61 

49 

60 

67 

63 

65 

73 

75 

67 

53 

51 



corn 



Workday 
probab 1 1 ities 






Wheat 



Corn 



GSL = 177 days 
Planting date is May 1 
Mature date is Aug. 19 
Harvest Period Aug. 20 

to Sept . 8 (20 days) 
GSL is 177 days . * . 
L is 50% 
. *. WP =(96t-98)/2 = 97 



GSL = 110 days May 25 to Sept. 11 

Plant date May 25 

Mature date Sept. 11 

GSL > 110 days . ' . L = 50% 

Harvest period Sept. 12 to Sept. 28 

WP = (96*96)/2=96 



Plant May 1 

Mature Sept. 9 

Harvest Sept. 10 = Oct. 18 

GSL 140 days L - 50/ 

WP = (96^96-^91 + 84) /4 = V 



Plant M.iy 2.5 

Mature Sept . 11 

GSL ■ 130 days . '. L = 100% 

WP = (67 + 63-t-f»5t-73)/4 = 67 



- 24 - 



Sample^ Cal Cul at ion: 

The following example out Lines Liu' manner in which the above procedures 
are used to calculate potential and anticipated net hiomass and dry matter 
yields for a corn crop for soil unit CI 7. 

1. Location: 42 N. Lat., 82 W. Long. - Southern Ontario 
Altitude: 175 m 

2 . Crowing Season Climate Information 

Crowing period: 159 days 

Start of growing season: May 13 

End of growing season: October 18 

Average mean 24 hour air temperature: 17.3 C 

Average maximum air temperature 22.9 C 

o 
Average minimum air temperature 11.8 C 

Average mean daytime temperature 20.9 C 

-2 -1 
Average incoming global solar radiation: 426.4 cal/cm day 

3 • Crop In form ation for C orn 

Days to maturity: 120 days 

Leaf area index (LAI) at the time of maximum growing rate: 4.0 

(from Table 2) 
Harvest index: 0.35 (from Table 2) 

4. Calculation of Rate of Potential Gross Biomass Production (b_,,) 

CM 



P - Maximum photosynthetic rate at 20.9 C: 53.97 kg ha hr (from 
equation hb) 

Y - Percentage difference in P relative to P =20 kg ha hr : 
169 percent (from equation 7) 

PAR - Average amount of photosynthetically active radiation on clear 

davs over the growing period: 355.8 cal cm day - (from Table 1) 

F - fraction of the daytime when the sky is overcast: 0.50 (from 
equation 5) 



verage rate of gross biomass production for perfectly clear days 
t P =20 kg ,ha hr""* over the growing season: 447.5 kg ha~" day 



b - av 
c 

a 

(from Table 1) . 

b - average rate of gross biomass production for perfectly clear days _^ 

at P = 20 kg ha _1 hr _1 over the growing season: 232.7 kg ha day 
(from Table 1) . 

b - rate of gross biomass production at P = 20 kg ha h at LAI=5: 
gm 20 339 kg ha-lday -1 (from equacion 4) 



- 25 - 



b - rate of gross biomass production at P =53.97 kg ha h at 
8 LAI = 5: 568.0 kg ha^day -1 (from equation 4 and 6a) 

MCGR - maximum crop growth rate at LAI=4.0: 0.936 (from equation 10) 

5. Calculation of Total Potential Net Biomass Production (BN ) and 
Yield < B W 

CT - maintenance respiration coefficient at 30 C: 0.0108 (for 
non-legume crop) . 

C T - maintenance respiration coefficient at 17. 3C: 0.00477 (from 
equation 3) 

B, T - total net biomass production at LAI - 5: 21.0 t/ha (from equation 2) 

N 

MCGR - maximum crop growth rate at LAI - 40: 0.936 (from equation 10) 

BN - actual crop potential net biomass production: 20.6 t/ha (from 
equation 11) , 

BY - actual crop potential dry matter yield: 7.2 t/ha (from equation 12) 

6 . Calculation of Moisture Stress and Workability Constraint 

a) Moisture Stress Losses : 

Ky - crop moisture yield response factor for corn - 1.25 (from 
Table 1, Doorembos and Kassam, 1979) . 

ETA/PE - growing season rate of actual to potential evapotranspi- 
ration - 0.894 (from equation 18) 

MSF - moisture stress factor - 0.868 (from equation 16a) 

b) Workability Losses : 

WP - workability parameter - 0.88 (see Table 5). 

7. Calculation of Anticipated Net Biomass (BN ) 



Dry Matter Yields (BY 4XTr J 

ANT 



B AX7m - 15.8 t/ha (from equation 13) 

ANT 

BY - 5.5 t/ha (from equation 14) 



— zo — 



The above example outlines the procedures used to quantify the 
constraint gree potential and actual anticipated yield for a corn 
crop during the growing season from generalized crop photo synthetic 
and respiration responses to average climatic factors involving 
radiation, temperature and precipitation. It must be emphasized that 
the techniques are not intended for real time prediction of actual 
crop production. They are intended only for evaluating the long-term 
crop production capability on a continental basis from a climatic 
point of view assuming optimum soil conditions. Actual production 
values can fluctuate significantly from year to year depending on the 
particular crop cultivar, the actual soil and climate conditions 
during the year, and the farm practices employed. Despite this, 
however, it is assumed that the yield estimates provided are represen- 
tative of the long-term climatic capability and as such will provide 
useful input into land evaluation assessments. 

The above methodology was used in assessing the land suitability 
in Canada for the production of wheat, corn, soybens , potatoes and 
phaseolus beans. The manner in which it was applied as well as the 
results of the assessment are not presented here but are outlined in 
detail by Dumanski and Stewart (1981) . 

Finally, it should be noted that the procedures described herein, 
are essentially those developed by F.A.P. (1978). Where discrepancies 
exist, particularly in the growing season definition and the estimates 
of the agroclimatic constraints, deviations from or modifications to 
the F.A.O. methodology were undertaken only to better reflect Canadian 
climatic conditions and farming practices. This was done to make the 
best use of the existing data base to provide the most up-to-date yield 
production potential assessment. 



- 27 - 



Acknowledgements 

The methodology described in the preceding section was put together 
by the Agrometeorology Section as part of a study carried out in coopera- 
tion with the Land Use and Evaluation Section of the Land Resource 
Research Institute, Agriculture Canada. As such the author is greatly 
indebted to a number of individuals for their valuable contributions to 
the successful completion of this project. In particular, special 
acknowledgement is extended to: Dr. G. den Hartog from the Atmospheric 
Environment Service - Environment Canada for supplying the grid square 
climatic information; Dr. A. Kassam, consultant to the F.A.O. in Rome 
for his frank discussion and constructive criticism; Dr. R. Desjardins 
and Dr. J. Dumanski for reviewing this manuscript; Mr. D. Russelo, 
Mr. D. Chaput and Mr. R. van Eyk for their computer programming assistance; 
and Miss J. De Castro for her typing services. To the many others who 
participated in the project their unselfish contributions are greatfully 
acknowledged. 



- 28 - 



References 

Baier, W., Dyer, J. A. and Sharp, W.R. , 1979: The versatile soil moisture 
budget. Tech. Bull. 87. Agr. Canada, Agrometeorology Section, Land 
Resource Research Institute, Ottawa, Ontario. 52pp. 

Brooks, C.E.P., 1943: Interpolation tables for daily values of meteorolo- 
gical elements. Q.J.R. Meteorol. Soc. 69(300) :160-162 . 

Clayton, J.S., Ehrlich, W.A., Cann, D.B., Day, J.H. and Marshall, I.B., 
1977: Soils of Canada. Vols. 1 and 2. Res. Br., Canada Depth, of 
Agric, Ottawa. 239pp. 

De Wit, C.T., 1965: Photosynthesis of leaf canopies. Agric. Res. Report 
663. Centre for Agr. Publ. and Docu., Wageningen, The Netherlands. 

Doorenbos, J. and Kassam, A.H., 1979: Yield response to water. F.A.O. 
Irrigation and Drainage Paper 33, F.A.O. , Rome. 193pp. 

Dumanski, J. and Stewart, R. , 1981: Crop production potentials for land 
evaluation in Canada. 

F.A.O., 1978: Report on the Agro-ecological zones project: Vol. 1. 

Methodology and results for Africa. World Soil Resources Report 48, 
F.A.O., Rome. 158pp. 

McCree, K.J., 1974: Equations for the rate of dark respiration of white 
clover and grain sorghum, as functions of dry weight, photosynthetic 
rate and temperature. Crop Sci., 14:509-514. 

Penman, H.L., 1948: Natural evaporation from open water, bare soil, and 
grass. Proceedings of the Roy. Soc. London. A., 193:120-146. 

Ritchie, J.T., 1972: Model for predicting evaporation from a row crop 
with incomplete cover. Water Res. Res. 8(5) :1204-1213 . 

Ritchie, J.T., 1974: Evaluating irrigation needs for southeastern 
U.S.A., _In: Contribution of Irrigation and Drainage to World 
Food Supply, ASCE, Biloxi, Mississippi, pp. 262-279. 

Sly, W.K. and Coligado, M.C., 1974: Agroclimatic maps for Canada - 

Derived data: moisture and critical temperatures near freezing. 
Tech. Bull. 81, Agr. Canada, Agrometeorological Res. and Serv., 
C.B.R.I., Ottawa, Canada, pp.31. 

Thorns, G.N., 1971: Physiological factors limiting the yield of arable 
crops. In: Potential Crop Production - A Case Study. Edited by 
P.F. Wareing and J. P. Cooper, Heinmann Education Books, pp. 143-158. 



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Van Ittersum, A., 1972: A calculation of potential rice yields. 
Netherlands J. Agr. Sci. 20:10-21. 

Watson, D.J., 1971: Size, structure, and activity of the productive 
system of crops. In: Potential Crop Production - A Case Study. 
Edited by P.F. Wareing and J. P. Cooper, Heinmann Education Books 
pp. 76-88. 




3 1073 0011& 071 £